MARIA M4: clinical evaluation of a prototype ultrawideband radar scanner for breast cancer detection

Preece, AW; Craddock, Ian J; Shere, Michael; Jones, Lyn; Winton, Helen L

doi:10.1117/1.jmi.3.3.033502

Cited by 154 publications

(154 citation statements)

References 40 publications

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“…In the field of medicine, microwaves have been established as a good solution for treatment of cancer (hyperthermia and ablation) and for monitoring of vital signs . Furthermore, there is ongoing research studying microwave imaging for image‐based diagnosis in an extensive number of applications: acute ischemia or cerebral hemorrhage, pulmonary edema, urinary incontinence, osteoporosis, and breast cancer detection; some of these studies are already in clinical phases . As far as we know, microwave imaging has never been proposed before for endoscopic applications.…”

Section: Introductionmentioning

confidence: 99%

Dielectric properties of colon polyps, cancer, and normal mucosa: Ex vivo measurements from 0.5 to 20 GHz

et al. 2018

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Section: Introductionmentioning

confidence: 99%

Dielectric properties of colon polyps, cancer, and normal mucosa: Ex vivo measurements from 0.5 to 20 GHz

et al. 2018

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“…The shortcomings of mammography, such as relatively low sensitivity, radiation exposure, and uncomfortable breast compression, have resulted in research into alternative methods of breast imaging. Microwave breast imaging (MBI) has been highlighted as an exciting method for the detection of diseased breast tissue, offering a potential non-ionizing, non-compressive approach to breast cancer diagnosis [53] and monitoring the effect of neoadjuvant chemotherapy [54]. The potential of microwave imaging for clinical application has been researched over the last forty years [55].…”

Section: Microwave Breast Imaging (Mbi)mentioning

confidence: 99%

“…Outcomes from the Multistatic Array Processing for Radiowave Image Acquisition (MARIA ® ) systems, developed at the University of Bristol, UK, have been published since 2010, and report the largest outcomes from operational MBI systems. Six clinical studies have been reported describing the use of this radar-based system ranging in size from 86 patients [53] to 223 patients [68]. This system was designed with a 60-element antenna array system that consists of 60 wide-slot antenna elements positioned in a hemispherical arrangement.…”

Section: Microwave Breast Imaging (Mbi)mentioning

confidence: 99%

Breast Cancer Detection—A Synopsis of Conventional Modalities and the Potential Role of Microwave Imaging

et al. 2020

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Global statistics have demonstrated that breast cancer is the most frequently diagnosed invasive cancer and the leading cause of cancer death among female patients. Survival following a diagnosis of breast cancer is grossly determined by the stage of the disease at the time of initial diagnosis, highlighting the importance of early detection. Improving early diagnosis will require a multi-faceted approach to optimizing the use of currently available imaging modalities and investigating new methods of detection. The application of microwave technologies in medical diagnostics is an emerging field of research, with breast cancer detection seeing the most significant progress in the last twenty years. In this review, the application of current conventional imaging modalities is discussed, and recurrent shortcomings highlighted. Microwave imaging is rapid and inexpensive. If the preliminary results of its diagnostic capacity are substantiated, microwave technology may offer a non-ionizing, non-invasive, and painless adjunct or stand-alone modality that could possibly be implemented in routine diagnostic breast care. Author Contributions: Conceptualization, B.M.M., D.O.'L, S.A.E., and M.J.K.; writing-original draft preparation, B.M.M., D.O.'L and S.A.E., writing-review and editing, B.M.M., D.O.'L, S.A.E., and M.J.K.; supervision, M.J.K.; funding acquisition, M.J.K.; All authors have read and agreed to the published version of the manuscript.

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“…Microwave imaging (MWI) has been widely studied as a potential imaging technique for early-stage screening and for monitoring breast cancer. [2][3][4] This technology has many attractive features, namely, it is noninvasive, nonionizing, uses low power and is potentially low-cost. Microwave imaging exploits the differences in dielectric properties between the different tissues within the breast and cancerous masses at microwave frequencies.…”

Section: Introductionmentioning

confidence: 99%

Classification of breast tumor models with a prototype microwave imaging system

et al. 2020

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Purpose: The assessment of the size and shape of breast tumors is of utter importance to the correct diagnosis and staging of breast cancer. In this paper, we classify breast tumor models of varying sizes and shapes using signals collected with a monostatic ultra-wideband radar microwave imaging prototype system with machine learning algorithms specifically tailored to the collected data. Methods: A database comprising 13 benign and 13 malignant tumor models with sizes between 13 and 40 mm was created using dielectrically representative tissue mimicking materials. These tumor models were placed inside two breast phantoms: a homogeneous breast phantom and a breast phantom with clusters of fibroglandular mimicking tissue, accounting for breast heterogeneity. The breast phantoms with tumors were imaged with a monostatic microwave imaging prototype system, over a 1-6 GHz frequency range. The classification of benign and malignant tumors embedded in the two breast phantoms was completed, and tumor classification was evaluated with Principal Component Analysis as a feature extraction method, and tuned Na€ ıve Bayes (NB), decision trees (DT), and knearest neighbours (kNN) as classifiers. We further study which antenna positions are better placed to classify tumors, discuss the feature extraction method and optimize classification algorithms, by tuning their hyperparameters, to improve sensitivity, specificity and the receiver operating characteristic curve, while ensuring maximum generalization and avoiding overfitting and data contamination. We also added a realistic synthetic skin response to the collected signals and examined its global effect on classification of benign vs malignant tumors. Results: In terms of global classification performance, kNN outperformed DT and NB machine learning classifiers, achieving a classification accuracy of 96.2% when classifying between benign and malignant tumor phantoms in a homogeneous breast phantom (both when the skin artifact is and is not considered). Conclusions:We experimentally classified tumor models as benign or malignant with a microwave imaging system, and we showed a methodology that can potentially assess the shape of breast tumors, which will give further insight into the correct diagnosis and staging of breast cancer.

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MARIA M4: clinical evaluation of a prototype ultrawideband radar scanner for breast cancer detection

Cited by 154 publications

References 40 publications

Dielectric properties of colon polyps, cancer, and normal mucosa: Ex vivo measurements from 0.5 to 20 GHz

Dielectric properties of colon polyps, cancer, and normal mucosa: Ex vivo measurements from 0.5 to 20 GHz

Breast Cancer Detection—A Synopsis of Conventional Modalities and the Potential Role of Microwave Imaging

Classification of breast tumor models with a prototype microwave imaging system

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